GNPS - Investigating/Evaluating sample normalization methods for mass spectrometry-based multi-omics and the application to a neurodegenerative mouse model

Omics research targets biomolecules such as proteins, lipids, and metabolites, providing a comprehensive view of biological systems. The field has seen significant advancements with the development of mass spectrometry (MS). However, the accuracy of quantitative analyses is crucial due to the expansion of omics applications. Accurate comparisons depend on sample preparation and normalization methods. Some biological samples, like tissues and feces, present inherent variations that challenge accuracy. Normalization aims to reduce these variations through pre-acquisition methods that equalize biomolecule content and post-acquisition methods that adjust instrument signals. Most research has focused on single-omics data and post-acquisition normalization. However, multi-omics research, essential for understanding complex biological systems, has not thoroughly evaluated normalization methods. This study evaluates three pre-acquisition normalization methods using methanol-chloroform-water extraction to enhance multi-omics data analysis. Our multi-omics data shows that the data is significantly different depending on the normalization methods and our optimized normalization method showed that there were significant multi-omics profile changes even with young mice tissue samples. Based on these findings, we suggest sample normalization based on total protein amount to minimize the sample variation and get an accurate comparison for multi-omics.